通过可解释的机器学习模型剖析网络攻击的预测因素.
Wenfeng Zhu1,2,3, Kai Wang1,2,3, Songyu Liu1,2,3
1Key Research Base of Humanities and Social Sciences of the Ministry of Education, Academy of Psychology and Behavior, Tianjin Normal University, Tianjin, China.
Aggressive behavior
|December 18, 2024
概括
这项研究确定了影响大学生网络攻击的关键因素. 防护因素,如反欺凌态度,可以显著减少风险因素的影响,如暴力态度.
科学领域:
- 心理学 心理学 心理学
- 犯罪学 犯罪学
- 数据科学数据科学数据科学
背景情况:
- 一般攻击模型 (GAM) 认为网络攻击是由个人和情境因素引起的.
- 以前使用线性模型的研究过于简化了这些因素的复杂相互作用.
- 需要对多种风险和保护因素进行全面分析.
研究的目的:
- 识别和排名网络攻击中风险和保护因素的重要性.
- 通过使用先进的机器学习技术来研究这些因素之间的相互作用.
- 为通用侵略模型 (GAM) 提供经验支持和扩展.
主要方法:
- 使用光梯度提升机 (LightGBM) 进行因子识别和排名.
- 使用SHAP (夏普利添加式计算) 来估计个别变量的预测效应.
- 进行二维部分依赖 (PD) 图表来分析预测器相互作用.
主要成果:
- 确定的前五个因素是:对暴力的态度,复仇的动机,反欺凌的态度,道德的脱离和愤怒的反思.
- 在保护因素 (反欺凌态度,道德推理) 和风险因素 (对暴力的态度,复仇动机,道德脱离,愤怒反) 之间发现了显著的相互作用.
- 高水平的保护因素被证明可以减轻风险因素对网络攻击的影响.
结论:
- 这些发现支持并扩展了网络攻击的背景下通用攻击模型 (GAM).
- 该研究强调了保护因素在缓解网络攻击方面的关键作用.
- 结果为开发干预措施减少中国大学生网络攻击提供了实际意义.
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